Pydantic AI Observability & Monitoring with OpenTelemetry

Pydantic AI has introduced observability and monitoring for its AI agents using OpenTelemetry, enabling users to export logs, traces, and metrics to SigNoz for real-time visibility into latency, error rates, and usage trends. The integration allows developers to instrument Pydantic AI applications with minimal code changes, leveraging SigNoz dashboards and alerts to debug issues and optimize performance.

Overview This guide walks you through setting up observability and monitoring for Pydantic AI API using OpenTelemetry https://opentelemetry.io/ and exporting logs, traces, and metrics to SigNoz. With this integration, you can observe model and agent performance, capture request/response details, and track system-level metrics in SigNoz, giving you real-time visibility into latency, error rates, and usage trends for your Pydantic AI applications. Instrumenting Pydantic AI in your AI applications with telemetry ensures full observability across your AI workflows, making it easier to debug issues, optimize performance, and understand user interactions. By leveraging SigNoz, you can analyze correlated traces, logs, and metrics in unified dashboards, configure alerts, and gain actionable insights to continuously improve reliability, responsiveness, and user experience. Prerequisites - A SigNoz Cloud account https://signoz.io/teams/ with an active ingestion key - Internet access to send telemetry data to SigNoz Cloud Pydantic AI https://ai.pydantic.dev/ integrated into your Python application.- For Python: pip installed for managing Python packages and optional but recommended a Python virtual environment to isolate dependencies Monitoring Pydantic AI For more detailed info on instrumenting your Pydantic AI applications click here https://ai.pydantic.dev/logfire/ otel-without-logfire . No-code auto-instrumentation is recommended for quick setup with minimal code changes. It's ideal when you want to get observability up and running without modifying your application code and are leveraging standard instrumentor libraries. Step 1: Install the necessary packages in your Python environment. pip install \ opentelemetry-distro \ opentelemetry-exporter-otlp \ httpx \ opentelemetry-instrumentation-httpx \ pydantic-ai Step 2: Add Automatic Instrumentation opentelemetry-bootstrap --action=install Step 3: Instrument your Pydantic AI application After setting up the OpenTelemetry configurations for traces, logs, and metrics, initialize Pydantic AI instrumentation by calling Agent.instrument all : python from pydantic ai.agent import Agent Initialize Pydantic AI instrumentation Agent.instrument all This call enables automatic tracing, logs, and metrics collection for all Pydantic AI agents in your application. 📌 Note: Ensure this is called before any Pydantic AI related calls to properly configure instrumentation of your application Step 4: Run an example python from pydantic ai import Agent, RunContext import asyncio Agent.instrument all roulette agent = Agent 'openai:gpt-4o', deps type=int, system prompt= 'Use the roulette wheel function to see if the ' 'customer has won based on the number they provide.' , instrument=True @roulette agent.tool async def roulette wheel ctx: RunContext int , square: int - str: """check if the square is a winner""" return 'winner' if square == ctx.deps else 'loser' async def main : success number = 18 result = await roulette agent.run 'Put my money on square eighteen', deps=success number print result.output if name == ' main ': asyncio.run main 📌 Note: Pydantic AI supports a variety of model providers for LLMs. In this example, we're using OpenAI. Before running this code, ensure that you have set the environment variable OPENAI API KEY with your generated API key. Step 5: Run your application with auto-instrumentation OTEL RESOURCE ATTRIBUTES="service.name=<service name " \ OTEL EXPORTER OTLP ENDPOINT="https://ingest.<region .signoz.cloud:443" \ OTEL EXPORTER OTLP HEADERS="signoz-ingestion-key=<your-ingestion-key " \ OTEL EXPORTER OTLP PROTOCOL=grpc \ OTEL TRACES EXPORTER=otlp \ OTEL METRICS EXPORTER=otlp \ OTEL LOGS EXPORTER=otlp \ OTEL PYTHON LOG CORRELATION=true \ OTEL PYTHON LOGGING AUTO INSTRUMENTATION ENABLED=true \ opentelemetry-instrument <your run command is the name of your service <service name <region : Your SigNoz Cloud region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint <your-ingestion-key : Your SigNoz ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ - Replace <your run command with the actual command you would use to run your application. For example: python main.py Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted https://signoz.io/docs/ingestion/cloud-vs-self-hosted/ cloud-to-self-hosted . Code-based instrumentation gives you fine-grained control over your telemetry configuration. Use this approach when you need to customize resource attributes, sampling strategies, or integrate with existing observability infrastructure. Step 1: Install the necessary packages in your Python environment. pip install \ opentelemetry-api \ opentelemetry-sdk \ opentelemetry-exporter-otlp \ opentelemetry-instrumentation-httpx \ opentelemetry-instrumentation-system-metrics \ pydantic-ai Step 2: Import the necessary modules in your Python application Traces: python from opentelemetry import trace from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.exporter.otlp.proto.http.trace exporter import OTLPSpanExporter Logs: python from opentelemetry.sdk. logs import LoggerProvider, LoggingHandler from opentelemetry.sdk. logs.export import BatchLogRecordProcessor from opentelemetry.exporter.otlp.proto.http. log exporter import OTLPLogExporter from opentelemetry. logs import set logger provider import logging Metrics: python from opentelemetry.sdk.metrics import MeterProvider from opentelemetry.exporter.otlp.proto.http.metric exporter import OTLPMetricExporter from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader from opentelemetry import metrics from opentelemetry.instrumentation.system metrics import SystemMetricsInstrumentor from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor Step 3: Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud python from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.exporter.otlp.proto.http.trace exporter import OTLPSpanExporter from opentelemetry import trace import os resource = Resource.create {"service.name": "<service name "} provider = TracerProvider resource=resource span exporter = OTLPSpanExporter endpoint= os.getenv "OTEL EXPORTER TRACES ENDPOINT" , headers={"signoz-ingestion-key": os.getenv "SIGNOZ INGESTION KEY" }, processor = BatchSpanProcessor span exporter provider.add span processor processor trace.set tracer provider provider is the name of your service <service name → SigNoz Cloud trace endpoint with appropriate OTEL EXPORTER TRACES ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/traces → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted https://signoz.io/docs/ingestion/cloud-vs-self-hosted/ cloud-to-self-hosted . Step 4 : Setup Logs python import logging from opentelemetry.sdk.resources import Resource from opentelemetry. logs import set logger provider from opentelemetry.sdk. logs import LoggerProvider, LoggingHandler from opentelemetry.sdk. logs.export import BatchLogRecordProcessor from opentelemetry.exporter.otlp.proto.http. log exporter import OTLPLogExporter import os resource = Resource.create {"service.name": "<service name "} logger provider = LoggerProvider resource=resource set logger provider logger provider otlp log exporter = OTLPLogExporter endpoint= os.getenv "OTEL EXPORTER LOGS ENDPOINT" , headers={"signoz-ingestion-key": os.getenv "SIGNOZ INGESTION KEY" }, logger provider.add log record processor BatchLogRecordProcessor otlp log exporter Attach OTel logging handler to root logger handler = LoggingHandler level=logging.INFO, logger provider=logger provider logging.basicConfig level=logging.INFO, handlers= handler logger = logging.getLogger name is the name of your service <service name → SigNoz Cloud endpoint with appropriate OTEL EXPORTER LOGS ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/logs → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted https://signoz.io/docs/ingestion/cloud-vs-self-hosted/ cloud-to-self-hosted . Step 5 : Setup Metrics python from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.metrics import MeterProvider from opentelemetry.exporter.otlp.proto.http.metric exporter import OTLPMetricExporter from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader from opentelemetry import metrics from opentelemetry.instrumentation.system metrics import SystemMetricsInstrumentor from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor import os resource = Resource.create {"service.name": "<service-name "} metric exporter = OTLPMetricExporter endpoint= os.getenv "OTEL EXPORTER METRICS ENDPOINT" , headers={"signoz-ingestion-key": os.getenv "SIGNOZ INGESTION KEY" }, reader = PeriodicExportingMetricReader metric exporter metric provider = MeterProvider metric readers= reader , resource=resource metrics.set meter provider metric provider meter = metrics.get meter name turn on out-of-the-box metrics SystemMetricsInstrumentor .instrument HTTPXClientInstrumentor .instrument is the name of your service <service name → SigNoz Cloud endpoint with appropriate OTEL EXPORTER METRICS ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/metrics → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted https://signoz.io/docs/ingestion/cloud-vs-self-hosted/ cloud-to-self-hosted . 📌 Note: SystemMetricsInstrumentor provides system metrics CPU, memory, etc. , and HTTPXClientInstrumentor provides outbound HTTP request metrics such as request duration. Pydantic AI additionally exposes LLM specific metrics as part of their SDK. If you want to add custom metrics to your Pydantic AI application, see Python Custom Metrics . Step 6: Instrument your Pydantic AI application After setting up the OpenTelemetry configurations for traces, logs, and metrics, initialize Pydantic AI instrumentation by calling Agent.instrument all : python from pydantic ai.agent import Agent Initialize Pydantic AI instrumentation Agent.instrument all This call enables automatic tracing, logs, and metrics collection for all Pydantic AI agents in your application. 📌 Note: Ensure this is called before any Pydantic AI related calls to properly configure instrumentation of your application Step 7: Run an example python from pydantic ai import Agent, RunContext import asyncio roulette agent = Agent 'openai:gpt-4o', deps type=int, system prompt= 'Use the roulette wheel function to see if the ' 'customer has won based on the number they provide.' , instrument=True @roulette agent.tool async def roulette wheel ctx: RunContext int , square: int - str: """check if the square is a winner""" return 'winner' if square == ctx.deps else 'loser' async def main : success number = 18 result = await roulette agent.run 'Put my money on square eighteen', deps=success number print result.output if name == ' main ': asyncio.run main 📌 Note: Pydantic AI supports a variety of model providers for LLMs. In this example, we're using OpenAI. Before running this code, ensure that you have set the environment variable OPENAI API KEY with your generated API key. View Traces, Logs, and Metrics in SigNoz Your Pydantic AI commands should now automatically emit traces, logs, and metrics. You should be able to view traces in Signoz Cloud under the traces tab: When you click on a trace in SigNoz, you'll see a detailed view of the trace, including all associated spans, along with their events and attributes. You should be able to view logs in Signoz Cloud under the logs tab. You can also view logs by clicking on the “Related Logs” button in the trace view to see correlated logs: When you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes: You should be able to see Pydantic related metrics in Signoz Cloud under the metrics tab: When you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes: Dashboard You can also check out our custom Pydantic AI dashboard here https://signoz.io/docs/dashboards/dashboard-templates/pydantic-ai-dashboard/ which provides specialized visualizations for monitoring your Pydantic AI usage in applications. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.